domain
Finance
Finance
Finance
Finance
Trading
In finance, trading uses advanced algorithms and machine learning to analyse market trends and carry out trades efficiently. By processing large amounts of data in real time, these systems can spot profitable opportunities, manage risks, and improve investment strategies. Automated trading solutions allow for faster decision-making and can boost portfolio performance while reducing human error.
Algorithmic trading
Execute trades at optimal prices and speeds with help of the algorithms. By analysing market data and using complex mathematical models, we enable traders to capitalise on price movements and market inefficiencies in real time, making sure to reduce human error, enhance trading efficiency, and maximise returns.
Algorithmic trading
Execute trades at optimal prices and speeds with help of the algorithms. By analysing market data and using complex mathematical models, we enable traders to capitalise on price movements and market inefficiencies in real time, making sure to reduce human error, enhance trading efficiency, and maximise returns.
Algorithmic trading
Execute trades at optimal prices and speeds with help of the algorithms. By analysing market data and using complex mathematical models, we enable traders to capitalise on price movements and market inefficiencies in real time, making sure to reduce human error, enhance trading efficiency, and maximise returns.
Automated trading
Enable investors to set predefined trading strategies that execute trades automatically based on specific criteria. By leveraging algorithms and real-time market data, such systems remove emotional decision-making and ensure timely responses to market changes.
Automated trading
Enable investors to set predefined trading strategies that execute trades automatically based on specific criteria. By leveraging algorithms and real-time market data, such systems remove emotional decision-making and ensure timely responses to market changes.
Automated trading
Enable investors to set predefined trading strategies that execute trades automatically based on specific criteria. By leveraging algorithms and real-time market data, such systems remove emotional decision-making and ensure timely responses to market changes.
Sentiment analysis
Use natural language processing to gauge market sentiment from various sources, including news articles, social media, and financial reports. By analysing public opinion and emotional tone, we provide insights into how sentiment influences market movements.
Sentiment analysis
Use natural language processing to gauge market sentiment from various sources, including news articles, social media, and financial reports. By analysing public opinion and emotional tone, we provide insights into how sentiment influences market movements.
Sentiment analysis
Use natural language processing to gauge market sentiment from various sources, including news articles, social media, and financial reports. By analysing public opinion and emotional tone, we provide insights into how sentiment influences market movements.
Finance
Finance
Finance
Credit scoring models
Credit scoring models evaluate how reliable individuals and businesses are in repaying loans. They look at factors like payment history, income, and debt levels. By using predictive analytics and machine learning, these models can provide more accurate assessments of creditworthiness.
Bureau data scoring models
Leverage extensive credit bureau information to assess an individual's creditworthiness. By analysing historical credit data, payment history, and account utilisation rates, we develop predictive models that assign credit scores using classical statistical techniques and ensembling methods.
Bureau data scoring models
Leverage extensive credit bureau information to assess an individual's creditworthiness. By analysing historical credit data, payment history, and account utilisation rates, we develop predictive models that assign credit scores using classical statistical techniques and ensembling methods.
Bureau data scoring models
Leverage extensive credit bureau information to assess an individual's creditworthiness. By analysing historical credit data, payment history, and account utilisation rates, we develop predictive models that assign credit scores using classical statistical techniques and ensembling methods.
Questionnaires scoring models
Utilise customised surveys to gather qualitative data from applicants, enhancing traditional credit assessments. By applying scoring algorithms to analyse responses to key financial and behavioural questions, we create profiles that offer deeper understanding of customers’ creditworthiness.
Questionnaires scoring models
Utilise customised surveys to gather qualitative data from applicants, enhancing traditional credit assessments. By applying scoring algorithms to analyse responses to key financial and behavioural questions, we create profiles that offer deeper understanding of customers’ creditworthiness.
Questionnaires scoring models
Utilise customised surveys to gather qualitative data from applicants, enhancing traditional credit assessments. By applying scoring algorithms to analyse responses to key financial and behavioural questions, we create profiles that offer deeper understanding of customers’ creditworthiness.
Transaction data scoring models
Analyse an individual's spending behaviour through detailed transaction history. By employing machine learning techniques to evaluate factors such as income patterns, spending habits, and transaction frequency, we gain insights into financial stability and reliability.
Transaction data scoring models
Analyse an individual's spending behaviour through detailed transaction history. By employing machine learning techniques to evaluate factors such as income patterns, spending habits, and transaction frequency, we gain insights into financial stability and reliability.
Transaction data scoring models
Analyse an individual's spending behaviour through detailed transaction history. By employing machine learning techniques to evaluate factors such as income patterns, spending habits, and transaction frequency, we gain insights into financial stability and reliability.
Telecom based scoring
Utilise data from mobile and telecom providers to assess credit risk for individuals who lack traditional credit histories. By analysing payment patterns, usage behaviour, and account longevity, we develop predictive models that determine creditworthiness.
Telecom based scoring
Utilise data from mobile and telecom providers to assess credit risk for individuals who lack traditional credit histories. By analysing payment patterns, usage behaviour, and account longevity, we develop predictive models that determine creditworthiness.
Telecom based scoring
Utilise data from mobile and telecom providers to assess credit risk for individuals who lack traditional credit histories. By analysing payment patterns, usage behaviour, and account longevity, we develop predictive models that determine creditworthiness.
Finance
Finance
Finance
Fraud detection
Fraud detection systems use machine learning and other detection techniques to spot unusual patterns that could signal fraudulent activity. By examining transaction data in real time, these systems can quickly identify suspicious transactions for further checking. Additionally, they learn from new data to be ahead of emerging threats, ensuring a secure environment for both customers and businesses.
Transaction fraud
We develop a customised fraud detection system to monitor and analyse transaction patterns in real time. By assessing factors such as transaction amounts, locations, and frequencies, we enable machines to quickly identify anomalies that may indicate fraudulent activity.
Transaction fraud
We develop a customised fraud detection system to monitor and analyse transaction patterns in real time. By assessing factors such as transaction amounts, locations, and frequencies, we enable machines to quickly identify anomalies that may indicate fraudulent activity.
Transaction fraud
We develop a customised fraud detection system to monitor and analyse transaction patterns in real time. By assessing factors such as transaction amounts, locations, and frequencies, we enable machines to quickly identify anomalies that may indicate fraudulent activity.
Network analysis
Identify relationships and patterns within transactional networks to detect hidden fraud rings, coordinated attacks and other fraudulent activities. By applying graph analysis techniques, we visualise and analyse connections between accounts, transactions, and behaviours.
Network analysis
Identify relationships and patterns within transactional networks to detect hidden fraud rings, coordinated attacks and other fraudulent activities. By applying graph analysis techniques, we visualise and analyse connections between accounts, transactions, and behaviours.
Network analysis
Identify relationships and patterns within transactional networks to detect hidden fraud rings, coordinated attacks and other fraudulent activities. By applying graph analysis techniques, we visualise and analyse connections between accounts, transactions, and behaviours.
User behaviour analytics
User behaviour analytics is used to establish baseline profiles of normal user activity. By continuously monitoring deviations from these profiles—such as unusual login locations, transaction types, or spending patterns—we can quickly identify potential fraudulent behaviour.
User behaviour analytics
User behaviour analytics is used to establish baseline profiles of normal user activity. By continuously monitoring deviations from these profiles—such as unusual login locations, transaction types, or spending patterns—we can quickly identify potential fraudulent behaviour.
User behaviour analytics
User behaviour analytics is used to establish baseline profiles of normal user activity. By continuously monitoring deviations from these profiles—such as unusual login locations, transaction types, or spending patterns—we can quickly identify potential fraudulent behaviour.
Chat-based fraud detection
We build an integrated natural language processing solution to analyse interactions within chat environments, such as customer support and online banking. By examining conversational patterns, keywords, and user intent, we identify potential fraudulent activities in real time.
Chat-based fraud detection
We build an integrated natural language processing solution to analyse interactions within chat environments, such as customer support and online banking. By examining conversational patterns, keywords, and user intent, we identify potential fraudulent activities in real time.
Chat-based fraud detection
We build an integrated natural language processing solution to analyse interactions within chat environments, such as customer support and online banking. By examining conversational patterns, keywords, and user intent, we identify potential fraudulent activities in real time.
Let's talk with our CEO
Email: andrii.rohovyi@postdata.ai
Let's talk with our CEO
Email: andrii.rohovyi@postdata.ai
Let's talk with our CEO
Email: andrii.rohovyi@postdata.ai
Postdata. All right reserved. © 2024
Postdata. All right reserved. © 2024
Postdata. All right reserved. © 2024